Crypto-space steganography for 3D mesh models with greedy selection and shortest expansion

Data hiding in encrypted 3D mesh models has emerged as a promising crypto-space steganography technique. However, the existing methods have the potential to improve embedding capacity due to the underutilization of the model’s topological features. In this paper, we propose an innovative greedy selection and shortest expansion strategy to select a proper reference set of vertices. Subsequently, the multi-MSB prediction and entropy coding are leveraged to further reduce the redundancy in the vertex coordinates for data embedding. By combining the new strategy and the efficient compressing of the embeddable vertices, we can raise the vertex utilization rate to approximately 90%. Experimental results show that our proposed scheme outperforms state-of-the-art methods, offering a substantial improvement in data payload for reversible data hiding in encrypted 3D mesh models.